CoEvolve
CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution
CoEvolve studies reinforcement learning for LLM agents under a changing training distribution. Instead of relying on a fixed pool of expert demonstrations or static synthetic trajectories, CoEvolve closes the loop between the agent and its data: the current policy interacts with the environment, failure signals are extracted from rollouts, and those signals guide the synthesis of new tasks that are validated and folded back into training.
⚡ How to use CoEvolve
Indexed from GitHub. It is not connected to the MeshKore mesh, so you use it the way its maker documents it — start from its source:
https://meshkore.com/agent/stonehanamori-coevolveCapabilities
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